Sojourn Time Research Articles

Progression from different blood glucose states to cardiovascular diseases: a prospective study based on multi-state model.

To quantify the trajectories from normoglycaemia to prediabetes, subsequently to type 2 diabetes (T2DM), cardiovascular diseases (CVD), and cardiovascular death, and the effects of risk factors on the rates of transition. We used data from the Jinchang cohort of 42,585 adults aged 20-88 free of coronary heart disease (CHD) and stroke at baseline. A multistate model was applied for analyzing the progression of CVD and its relation to various risk factors. During a median follow-up of 7 years, 7,498 participants developed prediabetes, 2,307 developed T2DM, 2,499 developed CVD, and 324 died from CVD. Among 15 postulated transitions, transition from comorbid CHD and stroke to cardiovascular death had the highest rate (157.21/1000 person-years), followed by transition from stroke alone to cardiovascular death (69.31/1000 person-years.), and transition from prediabetes to normoglycaemia (46.51/1000 person-years). Prediabetes had a sojourn time of 6.77 years, and controlling weight, blood lipids, blood pressure, and uric acid within normal limits may promote reversion to normoglycaemia. Among transitions to CHD alone and stroke alone, transition from T2DM had the highest rate (12.21/1000 and 12.16/1000 person-years), followed by transition from prediabetes (6.81/1000 and 4.93/1000 person-years), and normoglycaemia (3.28/1000 and 2.39/1000 person-years). Age and hypertension were associated with an accelerated rate for most transitions. Overweight/obesity, smoking, dyslipidemia, and hyperuricemia played crucial but different roles in transitions. Prediabetes was the optimal intervention stage in the disease trajectory. The derived transition rates, sojourn time, and influence factors could provide scientific support for the primary prevention of both T2DM and CVD.

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure.

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

A Simple Cost-Effectiveness Model of Screening: An Open-Source Teaching and Research Tool Coded in R.

Applied cost-effectiveness analysis models are an important tool for assessing health and economic effects of healthcare interventions but are not best suited for illustrating methods. Our objective is to provide a simple, open-source model for the simulation of disease-screening cost-effectiveness for teaching and research purposes. We introduce our model and provide an initial application to examine changes to the efficiency frontier as input parameters vary and to demonstrate face validity. We described a vectorised, discrete-event simulation of screening in R with an Excel interface to define parameters and inspect principal results. An R Shiny app permits dynamic interpretation of simulation outputs. An example with 8161 screening strategies illustrates the cost and effectiveness of varying the disease sojourn time, treatment effectiveness, and test performance characteristics and costs on screening policies. Many of our findings are intuitive and straightforward, such as a reduction in screening costs leading to decreased overall costs and improved cost-effectiveness. Others are less obvious and depend on whether we consider gross outcomes or those net to no screening. For instance, enhanced treatment of symptomatic disease increases gross effectiveness, but reduces the net effectiveness and cost-effectiveness of screening. A lengthening of the preclinical sojourn time has ambiguous effects relative to no screening, as cost-effectiveness improves for some strategies but deteriorates for others. Our simple model offers an accessible platform for methods research and teaching. We hope it will serve as a public good and promote an intuitive understanding of the cost-effectiveness of screening.

Estimating bidirectional transition and post‐reversion of mild cognitive impairment

AbstractBackgroundThe natural trajectory of reversion from MCI to normal cognition (NC) and the cognitive pattern post‐reversion have been poorly explored in the existing literature. The main goals of this study were as follows: (1) to quantitatively predict bidirectional transitions of MCI (reversion to NC and progression to dementia); (2) to explore patterns of future cognitive changes post‐reversion; and (3) to estimate the effects of selected covariates on transition probabilities in the course of cognitive loss.MethodThe National Alzheimer’s Coordinating Center database was funded by a National Institute on Aging/National Institutes of Health grant (U24 AG072122). Data for this study comprised 24,220 observations from 6,651 participants with an MCI diagnosis at study entry during at least two visits between June 2005 and February 2021. Defining NC and MCI as transient states and dementia as an absorbing state, we explored the fate of MCI and future cognitive changes post‐reversion using the continuous‐time, multi‐state Markov model, which provides information on the transition probability between multiple states, hazard ratio, mean sojourn time, and total length of stay over 15 years.ResultTransition intensity of re‐progression to MCI for post‐reversion was 0.3168, 2.48 times greater than the progression from MCI to dementia or reversion from MCI to NC. For the post‐reversion participants, the chance of progressing to dementia increased by 2% per year. participants who re‐progressed to MCI had an approximately 40% probability of reversing to NC again over the 15‐year period. participants stayed in the post‐reversion NC state for 3.2667 years (range 2.9349–3.6360) and in the MCI state for 1.7605 years (range 1.5134–2.0480) before progressing to dementia. Age, cognitive function, daily activity ability, and depressive symptoms were significant predictors of progression to dementia.ConclusionThere were distinct transition dynamics between amnesic MCI (aMCI) and nonamnesic (naMCI). The post‐reversion participants remained at increased risk of re‐progression to MCI or progression to dementia in the long term. Findings in this study can serve as valuable references for relevant research and healthcare professionals to help understand the natural history of cognitive loss and create proactive plans and targeted interventions.

Equivalent-Input-Disturbance Based Robust Control Design for Fuzzy Semi-Markovian Jump Systems via the Proportional-Integral Observer Approach

This work focuses on the design of a unified control law, which enhances the accuracy of both the disturbance estimation and stabilization of nonlinear T-S fuzzy semi-Markovian jump systems. In detail, a proportional-integral observer based equivalent-input-disturbance (PIO-EID) approach is considered to model and develop the controller. The PIO approach includes a variable for relaxation in the system design along with an additional term for integration to improve the flexibility of the design and endurance of the system. The proposed stability criteria are formulated in the form of matrix inequalities using Lyapunov theory and depend on the sojourn time for robust control design. Final analyses are performed using MATLAB software with simulations to endorse the theoretical findings of this paper.

Usefulness of heart failure categories based on left ventricular ejection fraction

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): This study was sponsored by AstraZeneca. Introduction Heart failure, a common disease with a mortality on par with many cancers, is one of the costliest diseases to society in the western world. Current heart failure guidelines are based on categories of left ventricular ejection fraction (LVEF) and have recently introduced a narrow category with mid-range/mildly reduced ejection fraction (HFmrEF; LVEF 41-49%) between the previous categories with reduced (HFrEF; LVEF ≤40%) and preserved (HFpEF; LVEF ≥50%) ejection fraction. The grouping of continuous variables into narrow categories can be questioned if the variability is high. Purpose To determine the shape of the LVEF distribution and quantify the stability of LVEF categories over time in a large real-world heart failure population, in order to establish the usefulness of LVEF categories for treatment decisions. Methods Population-based observational cohort study of all new cases of chronic heart failure in Region Stockholm, Sweden, from 2016-01-01 until 2020-12-31. All available values of LVEF were collected over time and patients were followed until death or end of study. Mixed models were used to quantify within-person variance in LVEF, and Multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. Results During the study period, 8,455 patients were diagnosed with heart failure. LVEF values followed a perfect normal distribution. The standard deviation of the within-person variance in LVEF was 7.4%. Regardless of initial LVEF category, age or sex, the mean time spent in any LVEF category before transition to another category (mean sojourn time) was on average less than 0.6 years (Figure 1). The one-year probabilities of transitioning between states were substantial (Figure 2). Patients with HFmrEF had a probability of 1/4 of remaining in that category one year later; the probability of patients with HFrEF of remaining in that category one year later was 1/4 for women and 1/3 for men; and patients with HFpEF had a probability of remaining in that category of 2/3 for women and 1/2 for men. Conclusion In patients with heart failure, LVEF has considerable variability over time. Hence, LVEF has limited use for categorisation of the heart failure population into narrow categories.

On Queues with Working Vacation and Interdependence in Arrival and Service Processes

In this paper, we consider two queuing models. Model 1 considers a single-server working vacation queuing system with interdependent arrival and service processes. The arrival and service processes evolve by transitions on the product space of two Markovian chains. The transitions in the two Markov chains in the product space are governed by a semi-Markov rule, with sojourn times in states governed by the exponential distribution. In contrast, in the second model, we consider independent arrival and service processes following phase-type distributions with representation (α,T) of order m and (β,S) of order n, respectively. The service time during normal working is the above indicated phase-type distribution whereas that during working vacation is a phase-type distribution with representation (β,θS), 0<θ<1. The duration of the latter is exponentially distributed. The latter model is already present in the literature and will be briefly described. The main objective is to make a theoretical comparison between the two. Numerical illustrations for the first model are provided.

Monte Carlo Simulation Approach to Shipping Accidents Consequences Assessment

The purpose of this study is to present and apply an innovative technique to model environmental consequences of shipping accidents in relations to events initiating those accidents. The Monte Carlo simulation technique is used to model shipping accidents and chemical release consequences within the world’s sea and ocean waters. The model was created based on the previously designed novel general probabilistic approach to critical infrastructure accident consequences, including three models: the process of initiating events generated by a critical infrastructure accident, the process of environmental threats coming from released chemicals that are a result of initiating events, and the process of environmental degradation stemming from environmental threats. It is a new approach that has never been proposed and applied before. The Monte Carlo simulation method is used under the assumption of the semi-Markov model of these three processes. A procedure for the realization and generation of this process and evaluation of its characteristics is proposed and applied in the preparation of the C# program. Using this program, the processes’ characteristics are predicted for a specific sea area. Namely, for the considered processes, the limit values of transient probabilities between the states and the mean values of total sojourn times at the particular states for the fixed time are determined. The results obtained can be used practically by maritime practitioners involved in making decisions related to the safety of maritime transport and to mitigation actions concerned with maritime accidents.

A stochastic jump model applied to collaborative queue-based high energy laser defense

High energy lasers (HELs) are evolving to provide an effective solution for air and missile defense. The emergence of this technology comes at a similar time to the development of cooperative and collaborative defense systems that collect and communicate data to inform decisions. This paper proposes a stochastic jump method for modeling the performance of networked HELs, defending against aerial threats which follow a queueing methodology. By drawing on an existing method that quantifies performance using the sum of sojourn times in a stochastic jump process, the model can predict the probability of survival when multiple effectors are tasked in defending against an arbitrary number of threats. The model can be applied more generally to processes with both waiting time–dependent service and finite existence. Furthermore, a new HEL counteraction probability model is developed to enable the demonstration and comparison of three different system collaboration methods in a future warfare application. Results suggest the prevailing superimposing laser strategy may be less effective than simple one-to-one allocation of lasers to threats. There may also be merit in targeting separate components of a threat’s structure.

Latency and Task Loss Probability for NOMA Assisted MEC in Mobility-Aware Vehicular Networks

In vehicular networks, task offloading performance for mobile edge computing (MEC) is highly coupled with the mobility of vehicle terminal (VT), as the VT speed and arrival rate determine the number of VTs and their sojourn time on each road segment. Generally, the communication and computation resources of the MEC server are limited, and thus more VTs leads to less resources for each VT. In this paper, we develop an analytical non-orthogonal multiple access (NOMA) assisted MEC model to characterize VT mobility and analyze the impact of VT mobility on task offloading performance. In this model, each VT with random speed starts its offloading at any time and hopefully completes the task offloading before leaving the current road segment. The expressions of average latency and task loss probability are derived and validated by simulation results. Furthermore, the impact of VT mobility, especially the average VT speed, on latency performances is analyzed. It is also shown that task loss probability is mainly affected by VT arrival rate, and is approximately independent of average VT speed.

Distributed Filtering for Semi-Markov-Type Sensor Networks With Hybrid Sojourn-Time Distributions—A Nonmonotonic Approach

This article examines the distributed filtering problem for a general class of filtering systems consisting of distributed time-delayed plant and filtering networks with semi-Markov-type topology switching (SMTTS). The SMTTS implies the topology sojourn time can be a hybrid function of different types of probabilistic distributions, typically, binomial distribution used to model unreliable communication links between the filtering nodes and Weibull distribution employed to depict the cumulative abrasion failure. First, by properly constructing a sojourn-time-dependent Lyapunov-Krasovski function (STDLKF), both time-varying topology-dependent filter and topology-dependent filter are designed. Second, a novel nonmonotonic approach with less design conservatism is developed by relaxing the monotonic requirement of STDLKF within each topology sojourn time. Moreover, an algorithm with less computational effort is proposed to generate a semi-Markov chain from a given Markov renewal chain. Simulation examples, including a microgrid islanded system, are presented to testify the generality and elucidate the practical potential of the nonmonotonic approach.

A queueing‐theoretic framework for evaluating transmission risks in service facilities during a pandemic

We propose a new modeling framework for evaluating the risk of disease transmission during a pandemic in small‐scale settings driven by stochasticity in the arrival and service processes, that is, congestion‐prone confined‐space service facilities. We propose a novel metric, system‐specific basic reproduction rate, inspired by the “basic reproduction rate” concept from epidemiology, which measures the transmissibility of infectious diseases. We derive our metric for various queueing models of service facilities by leveraging a novel queueing‐theoretic notion: sojourn time overlaps. We showcase how our metric can be used to explore the efficacy of a variety of interventions aimed at curbing the spread of disease inside service facilities. Specifically, we focus on some prevalent interventions employed during the COVID‐19 pandemic: limiting the occupancy of service facilities, protecting high‐risk customers (via prioritization or designated time windows), and increasing the service speed (or limiting patronage duration). We discuss a variety of directions for adapting our transmission model to incorporate some more nuanced features of disease transmission, including heterogeneity in the population immunity level, varying levels of mask usage, and spatial considerations in disease transmission.

Modelled mortality benefits of multi-cancer early detection screening in England

BackgroundScreening programmes utilising blood-based multi-cancer early detection (MCED) tests, which can detect a shared cancer signal from any site in the body with a single, low false-positive rate, could reduce cancer burden through early diagnosis.MethodsA natural history (‘interception’) model of cancer was previously used to characterise potential benefits of MCED screening (based on published performance of an MCED test). We built upon this using a two-population survival model to account for an increased risk of death from cfDNA-detectable cancers relative to cfDNA-non-detectable cancers. We developed another model allowing some cancers to metastasise directly from stage I, bypassing intermediate tumour stages. We used incidence and survival-by-stage data from the National Cancer Registration and Analysis Service in England to estimate longer-term benefits to a cohort screened between ages 50–79 years.ResultsEstimated late-stage and mortality reductions were robust to a range of assumptions. With the least favourable dwell (sojourn) time and cfDNA status hazard ratio assumptions, we estimated, among 100,000 screened individuals, 67 (17%) fewer cancer deaths per year corresponding to 2029 fewer deaths in those screened between ages 50–79 years.ConclusionRealising the potential benefits of MCED tests could substantially reduce late-stage cancer diagnoses and mortality.

Effect of sojourn time distributions on the early dynamics of COVID-19 outbreak.

Compartmental models are commonly used in practice to investigate the dynamical response of infectious diseases such as the COVID-19 outbreak. Such models generally assume exponentially distributed latency and infectiousness periods. However, the exponential distribution assumption fails when the sojourn times are expected to distribute around their means. This study aims to derive a novel S (Susceptible)-E (Exposed)-P (Presymptomatic)-A (Asymptomatic)-D (Symptomatic)-C (Reported) model with arbitrarily distributed latency, presymptomatic infectiousness, asymptomatic infectiousness, and symptomatic infectiousness periods. The SEPADC model is represented by nonlinear Volterra integral equations that generalize ordinary differential equation-based models. Our primary aim is the derivation of a general relation between intrinsic growth rate r and basic reproduction number with the help of the well-known Lotka-Euler equation. The resulting equation includes separate roles of various stages of the infection and their sojourn time distributions. We show that estimates are considerably affected by the choice of the sojourn time distributions for relatively higher values of r. The well-known exponential distribution assumption has led to the underestimation of values for most of the countries. Exponential and delta-distributed sojourn times have been shown to yield lower and upper bounds of the values depending on the r values. In quantitative experiments, values of 152 countries around the world were estimated through our novel formulae utilizing the parameter values and sojourn time distributions of the COVID-19 pandemic. The global convergence, , has been estimated through our novel formulation. Additionally, we have shown that increasing the shape parameter of the Erlang distributed sojourn times increases the skewness of the epidemic curves in entire dynamics.

Dynamic Scheduling in a Partially Fluid, Partially Lossy Queueing System

We consider a single server queueing system with two classes of jobs: eager jobs with small sizes that require service to begin almost immediately upon arrival, and tolerant jobs with larger sizes that can wait for service. While blocking probability is the relevant performance metric for the eager class, the tolerant class seeks to minimize its mean sojourn time. In this article, we analyse the performance of each class under dynamic scheduling policies, where the scheduling of both classes depends on the instantaneous state of the system. This analysis is carried out under a certain fluid limit, where the arrival rate and service rate of the eager class are scaled to infinity, holding the offered load constant. Our performance characterizations reveal a (dynamic) pseudo-conservation law that ties the performance of both classes to the standalone blocking probabilities associated with the scheduling policies for the eager class. Furthermore, the performance is robust to other specifics of the scheduling policies. We also characterize the Pareto frontier of the achievable region of performance vectors under the same fluid limit, and identify a (two-parameter) class of Pareto-complete scheduling policies.

Reliability modeling of weighted-k-out-of-n: G system under multiple failure modes with dependent components

In engineering practice, some weighted-k-out-of-n: G systems often show multiple performance levels, resulting in multi-failure-mode mechanism. For these complex degradation systems, a novel reliability model with heterogeneous components is proposed on account of the number of working components, the total weight, and the sojourn time in some a stage, where different weights and thresholds are taken into account in different stages, and so do failure modes. After presenting the indispensable definitions of first passage time and the system lifetime, the system reliability indexes are derived by the Copula method to depict the dependence between components. Furthermore, maximum likelihood estimation of the Copula parameters is adopted and a Copula-based expression for component importance in each type is obtained. Finally, the reliability of the overall supercharging subsystem of an advanced fighter air energy system, as an example of weighted-k-out-of-n: G system with multi-failure modes is analyzed by the suggested model, which offers the comparison of independent and dependent cases. Meanwhile, sensitivity analysis is available to point out the effects on the system.

Fractal dimensions of the Rosenblatt process

The paper concerns the image, level and sojourn time sets associated with sample paths of the Rosenblatt process. We obtain results regarding the Hausdorff (both classical and macroscopic), packing and intermediate dimensions, and the logarithmic and pixel densities. As a preliminary step we also establish the time inversion property of the Rosenblatt process, as well as some technical points regarding the distribution of Z.

The Tiny-Tasks Granularity Trade-Off: Balancing Overhead Versus Performance in Parallel Systems

Models of parallel processing systems typically assume that one has <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> workers and jobs are split into an equal number of <inline-formula><tex-math notation="LaTeX">$k=l$</tex-math></inline-formula> tasks. Splitting jobs into <inline-formula><tex-math notation="LaTeX">$k &gt; l$</tex-math></inline-formula> smaller tasks, i.e. using “tiny tasks”, can yield performance and stability improvements because it reduces the variance in the amount of work assigned to each worker, but as <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> increases, the overhead involved in scheduling and managing the tasks begins to overtake the performance benefit. We perform extensive experiments on the effects of task granularity on an Apache Spark cluster, and based on these, develop a four-parameter model for task and job overhead that, in simulation, produces sojourn time distributions that match those of the real system. We also present analytical results which illustrate how using tiny tasks improves the stability region of split-merge systems, and analytical bounds on the sojourn and waiting time distributions of both split-merge and single-queue fork-join systems with tiny tasks. Finally we combine the overhead model with the analytical models to produce an analytical approximation to the sojourn and waiting time distributions of systems with tiny tasks which include overhead. We also perform analogous tiny-tasks experiments on a hybrid multi-processor shared memory system based on MPI and OpenMP which has no load-balancing between nodes. Though no longer strict analytical bounds, our analytical approximations with overhead match both the Spark and MPI/OpenMP experimental results very well.

Estimating Sojourn Time and Transition between Clinical States of HIV Patients under ART Follow up in Namibia

Background: Sojourn time refers to the amount of time a HIV patient spends in each clinical state in a single stay before he/she makes a transition to another state. HIV can be broken down into a number of intermediate states, based on CD4 counts. The four states of the Markov process of HIV are commonly defined as: S1: CD4 count &gt; 500 cells/microlitre of blood; S2: 350 &lt; CD4 count ≤ 500 cells/microlitre of blood; S3: 200 &lt; CD4 count ≤ 350 cells/microlitre of blood; S4: CD4 count ≤ 200 cells/microliter of blood.&#x0D; Aims: The aim of the study was to estimate sojourn and transition between clinical states of patients under ART in Namibia using homogenous semi-Markov processes, on data obtained from MoHSS.&#x0D; Methods: A retrospective study design was used to obtain data on 2422 patients who were observed 11028 times, during 2008 to 2017 follow up period. The four staged semi-Markov model was employed to estimate sojourn times and transition between clinical states.&#x0D; Results: Results indicates that 1637 (67.6%) were female and 785 (32.41%) were male .657(27.13%) patients started ART in state 1, 683(28.19%) patients started ART in state 2, 677(27.95%) patients started ART in state 3 and 405(16.72%) patients started ART in state 4, at treatment commencement (t = 0). As expected, the probabilities of transiting from good to worse states increased with time. After 6 months, the probabilities of transiting from state 1 to 3, and from state 1 to 4 are 0.023 and 0.004 respectively. Whereas after 12 months, the probabilities of transiting from state 1 to 3, and from state 1 to 4 are 0.059 and 0.010 respectively. As time increased the probabilities to remain in the same state is decreasing (probabilities of remaining in state 1 after 6, 12 and 18 months is 0.804, 0.698 and 0.633). Sojourn times for states 1, 2, 3 and 4 were 22, 8, 10 and 15 months respectively.&#x0D; Conclusions: Sojourn time is of interest in HIV modeling, as it gives a signal of how HIV is progressing. Longer sojourn times indicates slow HIV progression and shorter sojourn times indicates rapid HIV progression. As time increases, transition probabilities from good states to worse states increases.

Comparison of single server queuing performance measures using fuzzy queuing models and intuitionistic fuzzy queuing models with infinite capacity

This paper presents boundless capacity, one server’s fuzzy and intuitionistic fuzzy queuing models. This study’s primary objective is to demonstrate and compare the performance of a single server queuing model with infinite capacity using fuzzy queuing theory and intuitionistic fuzzy queuing theory. This article demonstrates that intuitionistic fuzzy theory performs better when solving queuing problems. Furthermore, by integrating fuzzy queuing models into an intuitionistic fuzzy framework, their relevance in authentic situations is augmented. The fuzzy queuing theory model’s performance measurements are delivered as a range of values, but the intuitionistic fuzzy queuing theory model offers a broad array of values. In this context, the arrival and the service rates are both triangular (TFN) and intuitionistic triangular fuzzy numbers (TIFN). An assessment is performed to determine the evaluation criteria, employing a design protocol in which the fuzzy values are taken as-is without being turned into crisp values. As a result, in an ambiguous environment, we can use the proposed approach to pick scientific findings. In this study, we are using the TFN in an intuitionistic fuzzy environment, compensating for the degree of stability and denial so that the sum of both virtues is never higher than one. We proffered many non-normal arithmetic techniques for this sort of fuzzified integer. The envisaged compositions are intuitive and concise, as they evolved by utilising canonical algebraic mathematics. In real-world situations, this tactic is simple and straightforward to enact. The nearest interval number is then used to round a TIFN. The key advantage of this strategy is that it allows us to quickly solve a constrained unrestrained optimization model with TIFN coefficients using a multi-section heuristic. The prevailing methodologies and initiatives are destined to be relevant to different types of updated decision-making obstacles in focusing on economic equity, funding, presidency, and environmental sciences, which will be the focus of our future research. And two numerical problems are solved to showcase the sustainability of the suggested technique. In this queuing model, we predict a variety of components, including prospective queue length, expected system length, and sojourn time in both the queue and the system. The statistical analysis reveals that the quantified performance indicators of the intuitionistic fuzzy queuing model agree well with the performance measurements of the fuzzy queuing model. Even though the average correlation between the two concepts is nearly equivalent, TIFN provides a more extensive range of possibilities than TFN. Despite the fact that fuzzy set theory is used to contend with unpredictability in decision-making circumstances, it only relates to the extent of membership and lacks a model for reluctance. The fact that each asset’s affirmation and deprivation levels are comprehended is the special feature of intuitionistic fuzzy sets. As a consequence, it becomes more meticulous, suitable, and generalizable.